75 lines
3.4 KiB
TypeScript
75 lines
3.4 KiB
TypeScript
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import { RunnableInterface } from "@langchain/core/runnables";
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import { BaseRetriever, type BaseRetrieverInput } from "@langchain/core/retrievers";
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import { Document } from "@langchain/core/documents";
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import { VectorStore } from "@langchain/core/vectorstores";
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import { BaseTranslator, BasicTranslator, FunctionalTranslator, StructuredQuery } from "@langchain/core/structured_query";
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import { CallbackManagerForRetrieverRun } from "@langchain/core/callbacks/manager";
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import { QueryConstructorRunnableOptions } from "../../chains/query_constructor/index.js";
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export { BaseTranslator, BasicTranslator, FunctionalTranslator };
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/**
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* Interface for the arguments required to create a SelfQueryRetriever
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* instance. It extends the BaseRetrieverInput interface.
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*/
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export interface SelfQueryRetrieverArgs<T extends VectorStore> extends BaseRetrieverInput {
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vectorStore: T;
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structuredQueryTranslator: BaseTranslator<T>;
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queryConstructor: RunnableInterface<{
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query: string;
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}, StructuredQuery>;
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verbose?: boolean;
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useOriginalQuery?: boolean;
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searchParams?: {
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k?: number;
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filter?: T["FilterType"];
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mergeFiltersOperator?: "or" | "and" | "replace";
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forceDefaultFilter?: boolean;
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};
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}
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/**
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* Class for question answering over an index. It retrieves relevant
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* documents based on a query. It extends the BaseRetriever class and
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* implements the SelfQueryRetrieverArgs interface.
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* @example
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* ```typescript
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* const selfQueryRetriever = SelfQueryRetriever.fromLLM({
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* llm: new ChatOpenAI(),
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* vectorStore: await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings()),
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* documentContents: "Brief summary of a movie",
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* attributeInfo: attributeInfo,
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* structuredQueryTranslator: new FunctionalTranslator(),
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* });
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* const relevantDocuments = await selfQueryRetriever.getRelevantDocuments(
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* "Which movies are directed by Greta Gerwig?",
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* );
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* ```
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*/
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export declare class SelfQueryRetriever<T extends VectorStore> extends BaseRetriever implements SelfQueryRetrieverArgs<T> {
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static lc_name(): string;
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get lc_namespace(): string[];
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vectorStore: T;
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queryConstructor: RunnableInterface<{
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query: string;
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}, StructuredQuery>;
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verbose?: boolean;
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structuredQueryTranslator: BaseTranslator<T>;
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useOriginalQuery: boolean;
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searchParams?: {
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k?: number;
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filter?: T["FilterType"];
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mergeFiltersOperator?: "or" | "and" | "replace";
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forceDefaultFilter?: boolean;
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};
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constructor(options: SelfQueryRetrieverArgs<T>);
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_getRelevantDocuments(query: string, runManager?: CallbackManagerForRetrieverRun): Promise<Document<Record<string, unknown>>[]>;
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/**
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* Static method to create a new SelfQueryRetriever instance from a
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* BaseLanguageModel and a VectorStore. It first loads a query constructor
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* chain using the loadQueryConstructorChain function, then creates a new
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* SelfQueryRetriever instance with the loaded chain and the provided
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* options.
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* @param options The options used to create the SelfQueryRetriever instance. It includes the QueryConstructorChainOptions and all the SelfQueryRetrieverArgs except 'llmChain'.
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* @returns A new instance of SelfQueryRetriever.
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*/
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static fromLLM<T extends VectorStore>(options: QueryConstructorRunnableOptions & Omit<SelfQueryRetrieverArgs<T>, "queryConstructor">): SelfQueryRetriever<T>;
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}
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